Intercollegiate perfectionistic athletes' perspectives on achievement: Contributions to the understanding and assessment of perfectionism in sport
Bibliographic record
Abstract
The purpose of this study was to explore perfectionistic athletes’ perspectives on achievement in sport. Male and female intercollegiate athletes whose Sport Multidimensional Perfectionism Scale 2 (Sport-MPS-2; Gotwals & Dunn, 2009) subscale profile reflected healthy perfectionism (n = 7) or unhealthy perfectionism (n = 11) were purposefully sampled and interviewed. Content analysis of the interview data revealed three themes: personal expectations, coping with challenge, and role of others. Although these themes were common to both healthy and unhealthy perfectionists, the content generally represented a dichotomy of positive and negative interpretations, respectively. Discussion explores the degree to which these findings provide insight into perfectionism among athletes, support use of the tripartite model (Stoeber & Otto, 2006) and anecdotal accounts of perfectionism (e.g., Burns, 1980; Hamachek, 1978) within sport, foster resolution of the healthy–unhealthy perfectionism debate, contribute to the development of the Sport-MPS-2, and advance understanding of the domain-specificity of perfectionism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".